Alzheimer disease (AD) is a multigenic and multifactorial condition with a common pathological hallmark, deposition of A? and tau proteins aggregates in the brain. A few genes have been directly involved in the protein deposition metabolism. Another 24 loci have been identified as risk factors for AD which have shed light into other impaired mechanisms. There is a fundamental gap in our understanding of how all these pathways are interrelated towards a same ending phenotype. Omic technologies have been instrumental in complementing our understanding of the pathways involved between disruption of particular loci and final pathology. However, each one of these studies only explains a modest portion of the pathology of AD, whilst complex diseases involve a highly dynamic and interactive system of molecular layers. The central hypothesis is that different molecular layers are interconnected in AD so that the dysregulation of any of these causes the ultimate AD phenotype (A? and tau proteins aggregates). Multi-omic analysis can provide an insight into how different molecular dimensions interact with each other, an insight that single omic data cannot provide. Also, there is limited availability of multi-omic data collected on the same group of individuals and tissue. The objective of this project is to identify dysregulated pathways consistent across molecilar layers. In the K99 phase of the award, I plan to generate single-omic profiles (transcriptomic, proteomic and metabolomic) from brain tissue from highly characterized individuals. I will also leverage existing GWAs data for these individuals to conduct pair-wise integrative analysis to identify common variants that act as genetic regulators (QTL) for the identified dysregulated molecular markers. To conduct these analyses, I will gain training in network and pathway analysis, but also in big data and machine learning methods. During this period, I will also receive training in handling of induced pluripotent stem cells (iPSc) and in functional analysis. Preliminary analysis using transcriptomic data have identified AGFG2 gene to be overexpressed across AD etiologies compared to controls. AGFG2 is an astrocyte expressed gene that seems to be involved in A? metabolism. During the K99 phase I will examine the role of AGFG2 in iPSC-derived astrocytes from AD patients' carriers of known pathogenic mutations (ADAD). Having acquired this knowledge, during the R00 phase I will explore whether dysregulation of AGFG2 has the same effect in ADAD as in iPSC-derived astrocytes from early onset and late onset AD patients. Finally, I will elevate the pair-wise integration of omic data to a meta-dimensional level. This will allow me to identify molecular signals (transcripts, proteins, metabolites) that are consistent across molecular layers. If successful, this project has the potential to reveal novel insights of AD biology, which will be of interest to the scientific community. In addition, with this award I will develop a translational profile which will be invaluable for establishing me as an independent investigator in the growing field of systems biology.